Linking Statistical Mobility Data with Electrical Distribution Network Infrastructure for Generating an Agent Population for Multi-agent Simulation of Electric Vehicles with Markov Chains
To create realistic travel chains Markov Chains and Monte Carlo method are used. This includes different travel purposes in different locations, information about departure/arrival times and the distance driven. Individual agents are then generated which represent the electric vehicle (EV) population of a certain area. Depending on the defined charging infrastructure in the specific grid and the chosen EV model, this agent population can be calibrated for low- and medium voltage grids of different areas as there are rural or urban areas. Different charging strategies are simulated and their effect to the power grid analyzed.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7284675&punumber%3D7284675%26filter%3DAND%28p_IS_Number%3A7297490%29%26pageNumber%3D2%26rowsPerPage%3D100&pageNumber=3&rowsPerPage=100
-
Supplemental Notes:
- Copyright © 2014, IEEE.
-
Corporate Authors:
Institute of Electrical and Electronics Engineers (IEEE)
3 Park Avenue, 17th Floor
New York, NY United States 10016-5997 -
Authors:
- Übermasser, Stefan
- Stifter, Matthias
- Lenz, Gernot
- Heilmann, Bernhard
-
Conference:
- 2014 International Conference on Connected Vehicles and Expo (ICCVE)
- Location: Vienna , Austria
- Date: 2014-11-3 to 2014-11-7
- Publication Date: 2015
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 518-519
- Monograph Title: 2014 International Conference on Connected Vehicles and Expo (ICCVE)
Subject/Index Terms
- TRT Terms: Electric vehicle charging; Electric vehicles; Grids (Transmission lines); Markov chains; Mobility; Monte Carlo method; Simulation; Trip purpose
- Subject Areas: Energy; Environment; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01615606
- Record Type: Publication
- ISBN: 9781479967308
- Files: TRIS
- Created Date: Oct 31 2016 9:43AM